Summary To exploit the benefits of massive multiple‐input multiple‐output (M‐MIMO) technology in scenarios where base stations (BSs) need to be cheap and equipped with simple hardware, the computational complexity of classical signal processing schemes for spatial multiplexing of users shall be reduced. This calls for suboptimal designs that perform well the combining/precoding steps and simultaneously achieve low computational complexities. An approach on the basis of the iterative Kaczmarz algorithm (KA) has been recently investigated, assuring well execution without the knowledge of second order moments of the wireless channels in the BS, and with easiness since no tuning parameters, besides the number of iterations, are required. In fact, the randomized version of KA (rKA) has been used in this context because of global convergence properties. Herein, modifications are proposed on this first rKA‐based attempt, aiming to improve its performance‐complexity trade‐off solution for M‐MIMO systems. We observe that long‐term channel effects degrade the rate of convergence of the rKA‐based schemes. This issue is then tackled herein by means of a hybrid rKA initialization proposal, which lands within the region of convexity of the algorithm and assures fairness to the communication system. The effectiveness of our proposal is illustrated through numerical results, which bring more realistic system conditions in terms of channel estimation and spatial correlation than those used so far. We also characterize the computational complexity of the proposed rKA scheme, deriving upper bounds for the number of iterations. A case study focused on a dense urban application scenario is used to gather new insights on the feasibility of the proposed scheme to cope with the inserted BS constraints.
The mechanisms used by avian strains of Escherichia coli to invade the respiratory epithelia, leading to septicemia in poultry, are not well-established. In this work, we show that resident murine peritoneal macrophages infected in vitro with an avian strain of E. coli underwent apoptosis 4 h after infection (55.6% of apoptosis in infected cells versus 3.5% in non-infected cells). Heat-inactivated bacteria did not induce apoptosis and the inhibition of phagocytosis by pretreatment of cells with cytochalasin D reduced the number of apoptotic cells from 55.6 to 13.9% (P 6 0.05), showing that the bacteria must be intracellularly located and viable to induce apoptosis. Therefore, these data suggest that induction of macrophage apoptosis may be a pathogenic mechanism employed by avian E. coli to circumvent the host defences and invade the respiratory epithelia. ß
Massive multiple-input-multiple-output (M-MIMO) features a capability for spatial multiplexing of large number of users. This number becomes even more extreme in extralarge (XL-MIMO), a variant of M-MIMO where the antenna array is of very large size. Yet, the problem of signal processing complexity in M-MIMO is further exacerbated by the XL size of the array. The basic processing problem boils down to a sparse system of linear equations that can be addressed by the randomized Kaczmarz (RK) algorithm. This algorithm has recently been applied to devise low-complexity M-MIMO receivers; however, it is limited by the fact that certain configurations of the linear equations may significantly deteriorate the performance of the RK algorithm. In this paper, we embrace the interest in accelerated RK algorithms and introduce three new RK-based low-complexity receiver designs. In our experiments, our methods are not only able to overcome the previous scheme, but they are more robust against inter-user interference (IUI) and sparse channel matrices arising in the XL-MIMO regime. In addition, we show that the RK-based schemes use a mechanism similar to that used by successive interference cancellation (SIC) receivers to approximate the regularized zero-forcing (RZF) scheme.Index Terms-massive MIMO; extra-large scale massive MIMO; randomized Kaczmarz algorithm; receiver design. I. INTRODUCTIONE ARLY deployments of fifth generation (5G) networks are already exploiting massive multiple-input multipleoutput (M-MIMO) technology to cope with the rapid growth in the number of users and data traffic [1]. The benefits from the M-MIMO topology come from the spatial multiplexing of the users on the same time-frequency resources. However, the common choice for compact antenna arrays limits the spatial dimension of such systems, reducing the performance gains achievable in practice. One way to enhance the promised benefits of M-MIMO is to scale up the number of antenna elements at the base station (BS). Systems that embrace antenna arrays of extremely large dimensions can better separate a large number of users, significantly increasing overall performance.
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